Abstract
The study of optical font recognition has becoming more popular nowadays. In line to that, global analysis approach is extensively used to identify various font type to classify writer identity. Objective of this paper is to propose an enhanced global analysis method. Based on statistical analysis of edge pixels relationships, a novel method in feature extraction for binary images has proposed. We test the proposed method on Arabic calligraphy script image for optical font recognition application. We classify those images using Multilayer Network, Bayes network and Decision Tree classifiers to identify the Arabic calligraphy type. The experiments results shows that our proposed method has boost up the overall performance of the optical font recognition.
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Bataineh, B., Abdullah, S.N.H.S., Omar, K. (2011). A Statistical Global Feature Extraction Method for Optical Font Recognition. In: Nguyen, N.T., Kim, CG., Janiak, A. (eds) Intelligent Information and Database Systems. ACIIDS 2011. Lecture Notes in Computer Science(), vol 6591. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20039-7_26
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DOI: https://doi.org/10.1007/978-3-642-20039-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20038-0
Online ISBN: 978-3-642-20039-7
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